Weighted Poisson-disk Resampling on Large-Scale Point Clouds
Authors: Xianhe Jiao, Chenlei Lv, Junli Zhao, Ran Yi, Yu-Hui Wen, Zhenkuan Pan, Zhongke Wu, Yong-Jin Liu
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments show that our method significantly improves the performance of large-scale point cloud resampling for different applications, and provides a highly practical solution. Experiments We evaluate the performance of the proposed resampling method. All experiments are processed on a computer equipped with an AMD Ryzen 7 5800H, 16GB RAM, RTX3060, and with Windows 11 as its running system and Visual Studio as the development platform. The experiments include the following parts: (1) we introduce the selected datasets for the measurement and comparison; (2) we evaluate the geometric quality for different resampling methods; (3) we show some downstream applications based on the resampling method; (4) we illustrate the computational efficiency and some other details of our methods for different levels of resampling tasks. The project is released1. |
| Researcher Affiliation | Academia | 1College of Computer Science and Technology, Qingdao University; 2College of Computer Science and Software Engineering, Shenzhen University; 3Department of Computer Science and Engineering, Shanghai Jiao Tong University; 4School of Computer Science & Technology, Beijing Jiaotong University, 5School of Artificial Intelligence, Beijing Normal University; 6Department of Computer Science and Technology, MOE-Key Laboratory of Pervasive Computing, Tsinghua University {EMAIL, EMAIL} |
| Pseudocode | Yes | Algorithm 1: The pipeline of WPD 1: Input: Raw point cloud P 2: Output: Resampled point cloud P 3: Implement voxelization for P 4: Compute Poisson-disk radius r by Eq. (4) 5: Set re = r/2 for edge points if used 6: while |P|/n > 1.05 and |P| < n do 7: Update r by Eq. (6) 8: Poisson-disk resampling for P 9: end while 10: for each pi P do 11: Extract k neighbors of pi 12: if pi is an edge point and its neighbors have normal points then 13: continue 14: end if 15: Map k neighbors and pi onto local tangent plane 16: Compute Voronoi cells for all points 17: Update pi by Eq. (8) 18: end for 19: Output resampled P |
| Open Source Code | Yes | The project is released1. 1github.com/vvvwo/Weighted-Poisson-disk-Resampling |
| Open Datasets | Yes | The data we typically handle are scene data, rather than traditional simple models. Based on the target, we collect indoor and outdoor scenes from relevant datasets, including RGB-D dataset (Lai et al. 2013), Building Net (Selvaraju et al. 2021), Sensat Urban (Hu et al. 2021), S3DIS (Armeni et al. 2016), Urban Scene3D (Lin et al. 2022), and Urban BIS (Yang et al. 2023). |
| Dataset Splits | No | The target of our scheme is to implement efficient resampling while considering geometric feature preservation on large-scale point clouds. The data we typically handle are scene data, rather than traditional simple models. Based on the target, we collect indoor and outdoor scenes from relevant datasets, including RGB-D dataset (Lai et al. 2013), Building Net (Selvaraju et al. 2021), Sensat Urban (Hu et al. 2021), S3DIS (Armeni et al. 2016), Urban Scene3D (Lin et al. 2022), and Urban BIS (Yang et al. 2023). For a fair comparison, we set the resampling point number to 50,000 for all models. The triangulation is implemented by Geomagic, which is a commercial mesh generation software. Based on the indoor models of the S3DIS dataset, we compare their segmentation accuracy. For the quantification of the performance improvement of our method, we computed the m Io U metrics for 10 scenes from S3DIS, as shown in Figure 8. |
| Hardware Specification | Yes | All experiments are processed on a computer equipped with an AMD Ryzen 7 5800H, 16GB RAM, RTX3060, and with Windows 11 as its running system and Visual Studio as the development platform. |
| Software Dependencies | No | All experiments are processed on a computer equipped with an AMD Ryzen 7 5800H, 16GB RAM, RTX3060, and with Windows 11 as its running system and Visual Studio as the development platform. The text mentions Visual Studio but not a specific version number. |
| Experiment Setup | Yes | Specifically, we first implement the voxelization for input point cloud, the voxel length lv is set to 0.05 max{l, h.w} by default. Then, we estimate SP as SP ml2 v, (3) where m is the voxel number, ml2 v means that we accumulate each face area from related voxel to represent the SP . Since most voxels have only one face corresponding to the region of the point cloud. Based on the Eq. (1) and Eq. (3), we achieve the new radius computed by m/(λnπ), (4) where λ is set to 0.68 according to the experimental results. We iteratively refine the radius r by scaling parameters θ1, θ2, and µ (θ1 = 1.8, θ2 = 3.0, µ = (|P | n)/2). By fine-tuning the sampling radius, we can further optimize the number of sampling points while maintaining the uniform point distribution. Since the process does not involve complex tangent space mapping or distance optimization, it only performs the original Poisson-disk sampling in each iteration, making the method highly efficient. To enhance the edge points, we halve their corresponding radius. In this way, the adaptive densities are generated during the initial Poisson resampling. By the step of Voronoi central displacement, we assess all edge points and keep their positions if their neighborhood includes normal points. |